Powering up a pantry near you

  • A Super Target deserves a Super List. In 2020, Target introduced Drive Up, a service that allows users to order online and pick up groceries the
    same day.
  • When Target launched this service, they were positioning themselves to take on a $17 billion dollar industry, making meal planners’ lives easier in
    the process.
  • The Target Super List
    uses computer vision and machine learning to scan
    users’ shelves, take inventory, and suggest recipes based on the available ingredients.
  • When an ingredient isn’t available, it can be easily added to a shopping list for Target Drive Up, reducing food waste and saving time for users.
  • A Super Target deserves a Super List. In 2020, Target introduced Drive Up, a service that allows users to order online and pick up groceries the
    same day.
  • When Target launched this service, they were positioning themselves to take on a $17 billion dollar industry, making meal planners’ lives easier in the process.
  • The Target Super List uses computer vision and machine learning to scan users’ shelves, take inventory, and suggest recipes based on the
    available ingredients.
  • When an ingredient isn’t available,
    it can be easily added to a shopping list for Target Drive Up, reducing food waste and saving time for users.
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Process

  • I started this project off with
    a design sprint involving the three main disciplines in product: Engineering,
    Product and Design.
  • Since this is a spec project,
    I had Chat GPT fill in for my usual collaborators, and it performed surprisingly well!
  • Product GPT and I were fairly aligned on the product vision, and Product GPT even suggested something
    I hadn’t considered.
  • Engineering GPT focused
    more on crypto and security concerns, which I found interesting. Once again,
    I was surprised by the
    quality of the suggestions.
  • I started this project off with a design sprint involving the three main disciplines in product: Design, Engineering, and Product.
  • Since this is a spec project, I had Chat GPT fill in for my usual collaborators, and it performed surprisingly well!
  • Product GPT and I were fairly aligned on the product vision, and Product GPT even suggested something I hadn’t considered.
  • Engineering GPT focused more on crypto and security concerns, which was interesting. Once again, I was surprised by the quality of the suggestions.

WIREFRAMES

  • The wireframe process was sparser than I expected, which I now recognize is the result of the backend being so robust.
  • I did miss a couple of screens, most notably the empty states, which came out during the
    hi-definition/prototyping
    design phase. This is part
    of why I love prototyping.
  • In my process, prototyping helps me fill in the gaps in the flows and empathize with the user more concretely.
  • Overall, I ended up covering approximately 90% of the screens I needed. There is definitely some room for improvement, but it’s not
    terrible either.
  • The wireframe process was sparser than I expected, which I now recognize is the result of the backend being so robust.
  • I did miss a couple of screens, most notably the empty states, which came out during the hi-definition/prototyping design phase. This is part of why I love prototyping.
  • In my process, prototyping helps me fill in the gaps in the flows and empathize with the user more concretely.
  • Overall, I ended up covering approximately 90% of the screens I needed. There is definitely some room for improvement, but it’s not terrible either.